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Use Case

Automated real estate property descriptions

Generative AI models can automate real estate property descriptions, saving agents significant time and improving accuracy. Language Learning Models (LLMs) like ChatGPT can be trained on databases of real estate listings and their features. The models can then generate detailed, convincing descriptions simply by inputting property details. These descriptions accurately highlight the best features of properties and may include competitive keywords increasing visibility of listings.

Image generators can create impressive and realistic visual tours of homes based on property details. AI tools can style these images, enhancing them to make the real estate property more appealing to potential buyers. Additionally, AI models can generate 3D renderings of proposed building plans to give buyers an immersive experience of walking through the unfinished property.

How to build with Clevis

Text Input

Collect user input for the property details

Prompt ChatGPT

Prompt ChatGPT to generate a property description based on user input

HTTP Request

Fetch property data from a real estate API based on user input

Display Output

Format and display the generated property description

This application is an excellent example of what you can build using Clevis. It's a tailored solution for the real estate sector, aptly named 'Automated real estate property descriptions'. Similar applications within the real estate arena or other sectors can also be built using the Clevis tool.

The application operates in a sequence of four steps. First, a Text Input step collects the property details from the user. These details are then passed on to the ChatGPT step. In this phase, ChatGPT, an AI model developed by OpenAI, is prompted to generate an engaging and informative property description based on the user's input.

Next, the application executes an Http Request step where it fetches additional property data from a specified real estate API, again based on the user input. This feature expands the details to offer more comprehensive property information.

Finally, the application formats and displays the output in the Display Output step. The system generates the final output, a well-rounded property description that fuses both the user input and the fetched API data. This output is then displayed to the user.

In essence, this application demonstrates the efficiency and versatility of using chatbots like ChatGPT and toolkits like Clevis for tailored AI solutions.

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